Imaging Features of Bone Tumors: Conventional Radiographs and MR Imaging Correlation

Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching

BACKGROUND

In medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) technique is gradually entering radiology. It converts two-dimensional images into three-dimensional images, and AI can assist in image diagnosis. However, the application of artificial intelligence in medical education is still in its early stages. The purpose of this study is to explore the application value of AISD software based on VDR technique in medical imaging practical teaching, and to provide a basis for improving medical imaging practical teaching.

METHODS

Totally 41 students majoring in clinical medicine in 2017 were enrolled as the experiment group. AISD software based on VDR was used in practical teaching of medical imaging to display 3D images and mark lesions with AISD. Then annotations were provided and diagnostic suggestions were given. Also 43 students majoring in clinical medicine from 2016 were chosen as the control group, who were taught with the conventional film and multimedia teaching methods. The exam results and evaluation scales were compared statistically between groups.

RESULTS

The total skill scores of the test group were significantly higher compared with the control group (84.51 ± 3.81 vs. 80.67 ± 5.43). The scores of computed tomography (CT) diagnosis (49.93 ± 3.59 vs. 46.60 ± 4.89) and magnetic resonance (MR) diagnosis (17.41 ± 1.00 vs. 16.93 ± 1.14) of the experiment group were both significantly higher. The scores of academic self-efficacy (82.17 ± 4.67) and self-directed learning ability (235.56 ± 13.50) of the group were significantly higher compared with the control group (78.93 ± 6.29, 226.35 ± 13.90).

CONCLUSIONS

Applying AISD software based on VDR to medical imaging practice teaching can enable students to timely obtain AI annotated lesion information and 3D images, which may help improve their image reading skills and enhance their academic self-efficacy and self-directed learning abilities.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study

OBJECTIVES

To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center.

METHODS

This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations.

RESULT

On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%).

CONCLUSION

The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists.

CLINICAL RELEVANCE STATEMENT

Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients.

KEY POINTS

• The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

EPIDEMIOLOGICAL PROFILE AND EVOLUTION OF ANKLE MUSCULOSKELETAL TUMORS

Objective

Characterizing ankle tumors, presenting the epidemiological profile of these lesions.

Methods

Retrospective observational case series study to evaluate the results of clinical and/or surgical treatments of patients with ankle tumors whose first visit occurred from 1990 to 2020. The dependent variables were: benign bone tumor, malignant bone tumor, benign soft tissue tumor, malignant soft tissue tumor, and infection. The independent variables were: sex, age; presence of symptoms (pain/local volume increase/fracture), duration of symptoms until treatment, diagnosis, treatment, and recurrence.

Results

In total, 70 patients were included-58.5% were women, with a mean age at the time of diagnosis of 21.66 years. Among all cases, 76% were bone tumor, 14% were soft tissue tumor, and 10% were infection. The mean age at the time of diagnosis was 21.7 ± 2.29 years. The overall prevalence of pain was 77.1%. In total, 55.6% patients had a general local volume increase 13.4% had fractures. The mean time from symptoms to treatment was 17.4 ± 4.61 months and the mean diagnosis time was 10.13 ± 0.86 months. Of all cases, 73.44% underwent surgical treatment and 22.64% had recurrence.

Conclusion

In this series, ankle tumors corresponded mainly to bone tumors. Benign tumors were the most prevalent type of tumor and the highest occurrence was among young people. Level of Evidence IV, Case Series.

Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model

OBJECTIVES

Automatic bone lesions detection and classifications present a critical challenge and are essential to support radiologists in making an accurate diagnosis of bone lesions. In this paper, we aimed to develop a novel deep learning model called You Only Look Once (YOLO) to handle detecting and classifying bone lesions on full-field radiographs with limited manual intervention.

METHODS

In this retrospective study, we used 1085 bone tumor radiographs and 345 normal bone radiographs from two centers between January 2009 and December 2020 to train and test our YOLO deep learning (DL) model. The trained model detected bone lesions and then classified these radiographs into normal, benign, intermediate, or malignant types. The intersection over union (IoU) was used to assess the model's performance in the detection task. Confusion matrices and Cohen's kappa scores were used for evaluating classification performance. Two radiologists compared diagnostic performance with the trained model using the external validation set.

RESULTS

In the detection task, the model achieved accuracies of 86.36% and 85.37% in the internal and external validation sets, respectively. In the DL model, radiologist 1 and radiologist 2 achieved Cohen's kappa scores of 0.8187, 0.7927, and 0.9077 for four-way classification in the external validation set, respectively. The YOLO DL model illustrated a significantly higher accuracy for intermediate bone tumor classification than radiologist 1 (95.73% vs 88.08%, p = 0.004).

CONCLUSIONS

The developed YOLO DL model could be used to assist radiologists at all stages of bone lesion detection and classification in full-field bone radiographs.

KEY POINTS

• YOLO DL model can automatically detect bone neoplasms from full-field radiographs in one shot and then simultaneously classify radiographs into normal, benign, intermediate, or malignant. • The dataset used in this retrospective study includes normal bone radiographs. • YOLO can detect even some challenging cases with small volumes.

Study on Key Technologies of Virtual Interactive Surgical Simulation for 3D Reconstruction of Medical Images

Medical computed tomography (CT), nuclear magnetic resonance imaging (MRI), and other imaging facility produce a large amount of medical image data which has great diagnosis value. Traditional three-dimensional reconstruction of medical images has high requirements for graphics acceleration hardware and the processing speed. In this study, VxWorks embedded real-time process feature is used for CT or MRI DICOM data to real restoration and establishment of virtual three-dimensional model for realizing volume reconstruction, maximum density projection, multiplane reconstruction, dynamic interactive cutting of any surface, dynamic display of three-dimensional model and two-dimensional sectional image, surgical path planning and interactive surgical simulation, and determine the best surgical scheme. The practical application shows that the virtual simulation environment supports the seamless transplantation of code, function debugging, and interaction and solves the issue of high requirements for hardware. It can meet the needs of scientific research and teaching for clinicians and medical imaging workers and can be widely used in the training of virtual surgical anatomy for medical students.

X-ray and MRI Correlation of Bone Tumors Using Histopathology As Gold Standard

Introduction

Bone tumors are a common pathology of the musculoskeletal system being frequently encountered by clinicians. Radiological workup is a mainstay in the diagnostic workup of bone tumors. This study aimed to highlight the importance of plain radiography and MRI in the diagnosis of bone tumors keeping histopathology as a gold standard. It is a descriptive validation study conducted in the Radiology Department of Pakistan Institute of Medical Sciences Islamabad.

Methodology

The study included 92 patients with suspected bone lesions. After taking a complete history and receiving informed written consent. X-rays radiographs and magnetic resonance imaging were performed. X-ray radiograph and magnetic resonance imaging parameters were recorded and compared with the histopathology of lesions as a standard.

Results

The mean age of patients was 30.50 ± 8.95 years. Of 92 patients examined on X-ray, 51 (55.4%) had lytic lesions, 34 (37.0%) had sclerotic lesions, and seven (7.6 %) had mixed lesions. MRI revealed the location of the lesion. There were 25 (27.2%) bone lesions in diaphysis, 19 (20.7%) in metaphysis, nine (9.8%) at meta-diaphysis, and 32 (34.8 %) in the meta-epiphyseal region. These findings were later on confirmed with histopathological results.

Conclusion

MRI can differentiate soft-tissue components and periosteal reactions. An X-ray radiograph can provide information about bony matrix and calcifications within tumors. After analysis of imaging findings and histopathological results, it is concluded that these modalities can be used to diagnose bone tumors with high diagnostic accuracy.

The Lodwick classification for grading growth rate of lytic bone tumors: a decision tree approach

The estimation of growth rate of lytic bone tumors based on conventional radiography has been extensively studied. While benign tumors exhibit slow growth, malignant tumors are more likely to show fast growth. The most frequently used algorithm for grading of growth rate on conventional radiography was published by Gwilym Lodwick. Based on the evaluation of the four descriptors (1) type of bone destruction (including the subdescriptor "margin" for geographic lesions), (2) penetration of cortex, (3) presence of a sclerotic rim, and (4) expanded shell, an overall growth grade (IA, IB, IC, II, III) can be assigned, with higher grade representing faster tumor growth. In this article, we provide an easy-to-use decision tree of Lodwick's original grading algorithm, suitable for teaching of students and residents. Subtleties of the grading algorithm and potential pitfalls in clinical practice are explained and illustrated. Exemplary conventional radiographs provided for each descriptor in the decision tree may be used as a guide and atlas for assisting in evaluation of individual features in daily clinical practice.

A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors

OBJECTIVES

To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion.

METHODS

In this retrospective study, data were collected with pathologically confirmed diagnoses of bone tumors between 2012 and 2019. Deep learning and machine learning fusion models were built to classify tumors as benign, malignant, or intermediate using conventional radiographs of the lesion and potentially relevant clinical data. Five radiologists compared diagnostic performance with and without the model. Diagnostic performance was evaluated using the area under the curve (AUC).

RESULTS

A total of 643 patients' (median age, 21 years; interquartile range, 12-38 years; 244 women) 982 radiographs were included. In the test set, the binary category classification task, the radiological model of classification for benign/not benign, malignant/nonmalignant, and intermediate/not intermediate had AUCs of 0.846, 0.827, and 0.820, respectively; the fusion models had an AUC of 0.898, 0.894, and 0.865, respectively. In the three-category classification task, the radiological model achieved a macro average AUC of 0.813, and the fusion model had a macro average AUC of 0.872. In the observation test, the mean macro average AUC of all radiologists was 0.819. With the three-category classification fusion model support, the macro AUC improved by 0.026.

CONCLUSION

We built, validated, and tested deep learning and machine learning models that classified bone tumors at a level comparable with that of senior radiologists. Model assistance may somewhat help radiologists' differential diagnoses of bone tumors.

KEY POINTS

• The deep learning model can be used to classify benign, malignant, and intermediate bone tumors. • The machine learning model fusing information from radiographs and clinical characteristics can improve the classification capacity for bone tumors. • The diagnostic performance of the fusion model is comparable with that of senior radiologists and is potentially useful as a complement to radiologists in a bone tumor differential diagnosis.

Pathological fracture in paediatric bone tumours and tumour-like lesions: A predictor of benign lesions?

OBJECTIVE

To determine the incidence and causes of pathological fractures in paediatric bone tumours and tumour-like lesions, and to determine if they are predictive of benign lesions.

METHODS AND MATERIALS

Retrospective review of children with suspected bone tumours referred to a specialist musculoskeletal oncology service between September 2019 and August 2020. Data recorded included patient age and gender, lesion location, the presence of a pathological fracture on the initial plain radiograph, and the final diagnosis made either by image-guided biopsy/curettage or based on typical imaging features.

RESULTS

231 patients were included with 233 lesions (138 males and 93 females with mean age 10.5 years, range 3 months-18 years). Final diagnosis was based on histology in 85 (36.5%) cases and imaging in 148 (63.5%) cases, 52 (22.3%) lesions classed as non-neoplastic, 139 (59.7%) as benign and 42 (18%) as malignant. Pathological fractures were seen in 41 cases (17.6%) at presentation, involving the humerus in 19 (46.3%), the femur in 14 (34.1%), the tibia in 3 (7.3%), the fibula and radius in two each (4.9%) and the second toe proximal phalanx in 1 (2.4%) (p < 0.001). The commonest underlying lesions included simple bone cyst (n = 17; 41.5%) and non-ossifying fibroma (n = 10; 24.4%). Only 4 cases (9.75%) were malignant, one case each of osteosarcoma, Ewing sarcoma, leukaemia and BCOR undifferentiated round cell sarcoma. Pathological fracture occurred in 27.7% of non-malignant lesions and 9.5% of malignant lesions, this difference being statistically significant (p < 0.001).

CONCLUSION

Pathological fractures were seen in 17.6% of paediatric bone tumours, tumour-like lesions, being significantly associated with humeral location and non-malignant diagnosis.

ADVANCES IN KNOWLEDGE

Demonstrates the frequency, location and underlying diagnosis of pathological fractures in paediatric bone tumour and tumour-like lesions.

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors

Objectives

To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors.

Materials and Methods

In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves.

Results

The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824, P < .001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high-density components, and cortex involvement.

Conclusions

This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting.